Table 3. Open avenues in dynamic... | Emerald Publishing

Table 3.

Open avenues in dynamic capabilities research

Research family	What current DC research has established	What remains unresolved	Illustrative research questions
Construct foundations, boundaries and integration	DCs have been defined as abilities, routines, processes, higher-order capabilities, and mechanisms of resource reconfiguration. Prior reviews have clarified definitional plurality and construct boundaries, but without fully resolving them (Barreto, 2010; Schilke et al., 2018)	The construct still risks slippage between capabilities, processes, practices, antecedents, and outcomes. The relationship between ordinary and dynamic capabilities also remains theoretically underdeveloped, particularly as new technologies increasingly automate operational activities while elevating higher-order orchestration (Helfat and Winter, 2011; Peteraf et al., 2013; Gernone and Teece, 2024)	What makes a capability genuinely dynamic? When does operational improvement become strategic reconfiguration? How do ordinary and dynamic capabilities co-evolve? How can DCs integrate adjacent theories without becoming conceptually overextended?
Microfoundations, managerial agency and multilevel capability formation	Research has shown that managerial cognition, emotion, attention, judgment, human capital, social capital, and agency shape DCs (Hodgkinson and Healey, 2011; Helfat and Peteraf, 2015)	The field still underspecifies how individual-level attributes become collective, organizational, and ecosystem-level capabilities. Non-cognitive and embodied foundations also remain underdeveloped (Helfat and Martin, 2015; Huy and Zott, 2019)	How does managerial judgment become organizational reconfiguration? How do affect, intuition, identity, and tacit sensitivity shape sensing and seizing? How do team and ecosystem interactions scale individual capabilities?
Temporality, history, learning and capability decay	DC research has emphasized path dependence, capability lifecycles, experience accumulation, learning, knowledge articulation, codification, and historically embedded routines (Zollo and Winter, 2002; Helfat and Peteraf, 2003)	The literature explains capability accumulation better than abandonment, decay, forgetting, and strategic uses of the past. Recent work opens this direction but does not yet provide a full temporal theory of DCs (Suddaby et al., 2020; Aoki, 2026)	When does history constrain renewal, and when does it enable transformation? How do firms unlearn obsolete capabilities? How do temporal orientations shape sensing, seizing, and transforming?
Performance, paradox, and empirical identification	Empirical studies show that DCs do not automatically improve performance. Their effects are contingent, nonlinear, and shaped by environmental dynamism, strategic fit, organizational structure, and capability configuration (Drnevich and Kriauciunas, 2011; Schilke, 2014a)	The field still lacks robust measures of DC deployment over time and has limited understanding of when DCs destroy value, produce overload, or create rigidity (Ringov, 2017; Laaksonen and Peltoniemi, 2018)	When do DCs enhance performance, and when do they become costly or counterproductive? Do DCs exhibit diminishing returns? How can sensing, seizing, transforming, and capability orchestration be measured dynamically and causally?
Digital, AI-enabled, and business-model dynamic capabilities	DC research has linked digital transformation to renewal of routines, structures, business models, platforms, and ecosystems. Business-model research also shows that DCs support value creation and capture (Teece, 2018a; Warner and Wäger, 2019)	It remains unclear whether AI and digital infrastructures merely support DCs or fundamentally reshape the architecture of capabilities by redistributing cognition, coordination, decision authority, and organizational boundaries. The relationship between AI, ordinary capabilities, dynamic capabilities, and human strategic agency remains theoretically underdeveloped (Chatterji et al., 2026; Csaszar et al., 2026; Gernone and Teece, 2024)	When does AI augment rather than substitute managerial judgment? When does it expand strategic imagination, experimentation, and renewal? When does it reinforce exploitation, suppress dissent, or produce algorithmic lock-in? How does AI transform the relationship between ordinary and dynamic capabilities? How do DCs enable business-model experimentation, scaling, and pivoting?
Ecosystems, networks, governance, environmental shaping and value appropriation	Recent research extends DCs beyond internal resource reconfiguration toward external-facing capabilities, ecosystem leadership, platform governance, foresight, networks, and environmental shaping (Helfat, 2022; Foss et al., 2023)	The literature still undertheorizes architectural governance, complementary assets, platform design, modularity, APIs, value appropriation, power, dependence, legitimacy, regulation, standards, and feedback loops between firm action and environmental change (Fergnani, 2022; Cristofaro et al., 2025; Gernone and Teece, 2024)	When do DCs restore fit, and when do they alter the conditions of fit? Who has authority to orchestrate ecosystems? When does ownership of complementary assets matter more than architectural control? How do APIs and modular architectures reshape ecosystem governance? How do firms simultaneously create and appropriate value while shaping markets, standards, institutions, and technological trajectories?
Grand challenges, sustainability, and pluralistic organizing	DC research has begun to examine sustainability transitions, circular economy innovation, stakeholder complexity, and pluralistic organizational contexts (Re et al., 2025; Hafeez et al., 2025)	The field still lacks a strong account of how DCs operate when firms must balance economic, social, environmental, and institutional demands across interconnected systems while maintaining long-term adaptability (Schilke et al., 2018; Pitelis et al., 2024)	How do DCs support net-zero and circular transitions? How do firms manage competing social, environmental, and economic logics without mission drift? How do DCs operate across supply chains, regulators, users, platforms, and communities?

Research family	What current DC research has established	What remains unresolved	Illustrative research questions
Construct foundations, boundaries and integration	DCs have been defined as abilities, routines, processes, higher-order capabilities, and mechanisms of resource reconfiguration. Prior reviews have clarified definitional plurality and construct boundaries, but without fully resolving them (Barreto, 2010; Schilke et al., 2018)	The construct still risks slippage between capabilities, processes, practices, antecedents, and outcomes. The relationship between ordinary and dynamic capabilities also remains theoretically underdeveloped, particularly as new technologies increasingly automate operational activities while elevating higher-order orchestration (Helfat and Winter, 2011; Peteraf et al., 2013; Gernone and Teece, 2024)	What makes a capability genuinely dynamic? When does operational improvement become strategic reconfiguration? How do ordinary and dynamic capabilities co-evolve? How can DCs integrate adjacent theories without becoming conceptually overextended?
Microfoundations, managerial agency and multilevel capability formation	Research has shown that managerial cognition, emotion, attention, judgment, human capital, social capital, and agency shape DCs (Hodgkinson and Healey, 2011; Helfat and Peteraf, 2015)	The field still underspecifies how individual-level attributes become collective, organizational, and ecosystem-level capabilities. Non-cognitive and embodied foundations also remain underdeveloped (Helfat and Martin, 2015; Huy and Zott, 2019)	How does managerial judgment become organizational reconfiguration? How do affect, intuition, identity, and tacit sensitivity shape sensing and seizing? How do team and ecosystem interactions scale individual capabilities?
Temporality, history, learning and capability decay	DC research has emphasized path dependence, capability lifecycles, experience accumulation, learning, knowledge articulation, codification, and historically embedded routines (Zollo and Winter, 2002; Helfat and Peteraf, 2003)	The literature explains capability accumulation better than abandonment, decay, forgetting, and strategic uses of the past. Recent work opens this direction but does not yet provide a full temporal theory of DCs (Suddaby et al., 2020; Aoki, 2026)	When does history constrain renewal, and when does it enable transformation? How do firms unlearn obsolete capabilities? How do temporal orientations shape sensing, seizing, and transforming?
Performance, paradox, and empirical identification	Empirical studies show that DCs do not automatically improve performance. Their effects are contingent, nonlinear, and shaped by environmental dynamism, strategic fit, organizational structure, and capability configuration (Drnevich and Kriauciunas, 2011; Schilke, 2014a)	The field still lacks robust measures of DC deployment over time and has limited understanding of when DCs destroy value, produce overload, or create rigidity (Ringov, 2017; Laaksonen and Peltoniemi, 2018)	When do DCs enhance performance, and when do they become costly or counterproductive? Do DCs exhibit diminishing returns? How can sensing, seizing, transforming, and capability orchestration be measured dynamically and causally?
Digital, AI-enabled, and business-model dynamic capabilities	DC research has linked digital transformation to renewal of routines, structures, business models, platforms, and ecosystems. Business-model research also shows that DCs support value creation and capture (Teece, 2018a; Warner and Wäger, 2019)	It remains unclear whether AI and digital infrastructures merely support DCs or fundamentally reshape the architecture of capabilities by redistributing cognition, coordination, decision authority, and organizational boundaries. The relationship between AI, ordinary capabilities, dynamic capabilities, and human strategic agency remains theoretically underdeveloped (Chatterji et al., 2026; Csaszar et al., 2026; Gernone and Teece, 2024)	When does AI augment rather than substitute managerial judgment? When does it expand strategic imagination, experimentation, and renewal? When does it reinforce exploitation, suppress dissent, or produce algorithmic lock-in? How does AI transform the relationship between ordinary and dynamic capabilities? How do DCs enable business-model experimentation, scaling, and pivoting?
Ecosystems, networks, governance, environmental shaping and value appropriation	Recent research extends DCs beyond internal resource reconfiguration toward external-facing capabilities, ecosystem leadership, platform governance, foresight, networks, and environmental shaping (Helfat, 2022; Foss et al., 2023)	The literature still undertheorizes architectural governance, complementary assets, platform design, modularity, APIs, value appropriation, power, dependence, legitimacy, regulation, standards, and feedback loops between firm action and environmental change (Fergnani, 2022; Cristofaro et al., 2025; Gernone and Teece, 2024)	When do DCs restore fit, and when do they alter the conditions of fit? Who has authority to orchestrate ecosystems? When does ownership of complementary assets matter more than architectural control? How do APIs and modular architectures reshape ecosystem governance? How do firms simultaneously create and appropriate value while shaping markets, standards, institutions, and technological trajectories?
Grand challenges, sustainability, and pluralistic organizing	DC research has begun to examine sustainability transitions, circular economy innovation, stakeholder complexity, and pluralistic organizational contexts (Re et al., 2025; Hafeez et al., 2025)	The field still lacks a strong account of how DCs operate when firms must balance economic, social, environmental, and institutional demands across interconnected systems while maintaining long-term adaptability (Schilke et al., 2018; Pitelis et al., 2024)	How do DCs support net-zero and circular transitions? How do firms manage competing social, environmental, and economic logics without mission drift? How do DCs operate across supply chains, regulators, users, platforms, and communities?

Source(s): Authors’ own work